Julio Acosta-Cabronero¹, Lara Z Diaz-de-Grenu¹, Joao MS Pereira¹, George Pengas¹, Guy B Williams¹, and Peter J Nestor^1
1Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom

In this study we tested the hypothesis that voxel-based morphometry (VBM) using the recently-developed T2-weighted SPACE acquisition would be more sensitive to grey matter pathology in Alzheimer’s disease (AD) than conventional T1-VBM using MPRAGE images with the same resolution. The distribution of abnormalities identified by T2-VBM, but not with T1-VBM, bore a striking resemblance to the distribution of amyloid plaque deposition in AD. This study demonstrates that T2-VBM is more sensitive to histopathological brain changes than the conventional T1-VBM method.

Hamied Ahmad Haroon^1,2, Heather Reynolds¹, Stephen F Carter^2,3, Karl V Embleton^2,4, Karl G Herholz^2,3, and Geoff J Parker^1,2
1Imaging Science & Biomedical Engineering, School of Cancer & Enabling Sciences, The University of Manchester, Manchester, England, United Kingdom, ²Biomedical Imaging Institute, The University of Manchester, Manchester, England, United Kingdom, ³Wolfson Molecular Imaging Centre, School of Cancer & Enabling Sciences, The University of Manchester, Manchester, England, United Kingdom, ⁴School of Psychological Sciences, The University of Manchester, Manchester, England, United Kingdom

We apply probabilistic characterization of grey matter diffusion complexity to patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), and healthy controls of similar age. We find statistical differences in the regional median probabilities of observing n distinct dominant diffusion orientations between these subject groups providing evidence for the microstructural changes in grey matter associated with these pathologies. We find that the degree of abnormality in grey matter complexity increases in AD relative to MCI, consistent with the concept of MCI being a prodromal state of AD.

Marco Bozzali¹, Geoff Parker², Laura Serra¹, Roberta Perri³, Franco Giubilei⁴, Camillo Marra⁵, Carlo Caltagirone³, and Mara Cercignani^1
1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, ²Imaging Science & Biomedical Engineering, University of Manchester, Manchester, United Kingdom,³Department of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy, ⁴Department of Neurology, II Faculty of Medicine, “Sapienza” University of Rome, Rome, ⁵Institute of Neurology, Università Cattolica, Rome, Italy

A recent application of anatomical connectivity mapping (ACM) to Alzheimer’s disease (AD) patients, has shown expected reductions as well as unexpected increases of structural brain connectivity. The latter finding was interpreted as a possible consequence of processes of brain plasticity driven by treatment with cholinesterase inhibitors (ChEIs). Here, we confirm and extend all preliminary findings by assessing ACM in a larger group of patients with AD, half of them under ChEIs medication and half drug naïve. This study further supports the hypothesis that ChEIs induce mechanisms of plasticity in AD brains, which may also interact with measures of global cognition.

Nicolas Robitaille¹, Abderazzak Mouiha¹, and Simon Duchesne^1,2
1Centre de recherche Université Laval Robert-Giffard, Québec, QC, Canada, ²Radiology, Université Laval, Quebec, QC, Canada

Structural MRI has been proposed to fulfill the role of quantitative biomarker in Alzheimer’s disease. We proposed a high-dimensional morphological metric extracted from T1-weighted MRI and now wish to demonstrate its robustness in a multi-centric setting. To form our metric we used data from two different studies, totaling 300 subjects. We tested the metric over the 797 subjects of the ADNI dataset, and found an average scan/repeat scan distance of 1.7%. In order to detect a 15% difference between groups, this minimum precision threshold results in an increase from 59 to 75 participants to reach identical power in a trial.

Andreia Vasconcellos Faria^1,2, Kyrana Tsapkini³, Jennifer Crinion⁴, Hangyi Jiang¹, Xin Li¹, Kenichi Oishi¹, Peter van Zijl¹, Michael Miller⁵, Argye Hillis³, and Susumu Mori^1
1Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Radiology, State University of Campinas, Campinas, SP, Brazil, ³Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁴Institute of Cognitive Neuroscience, University College London, ⁵Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Based on large-deformation diffeomorphic metric mapping and Atlas-based analysis, we developed a method to capture the anatomical features and classifying primary progressive aphasia (PPA) patients. Principal component analysis, multivariate techniques and predictive modeling were applied to a 32 PPA patients and 27 controls. The variables used to create and test the classification model were selected among the volumes of the 211 regions obtained from the automated 3D segmentation. The percentage of correct classification was 83% after two-level cross-validation. The results from this automated quantitative analysis can be user-friendly displayed and this method can be applied to routine clinical practice

Mark David Meadowcroft^1,2, Zachary George Herse¹, James R Connor³, and Qing X Yang^1
1Radiology - Center for NMR Research, Pennsylvania State University - College of Medicine, Hershey, PA, United States, ²DMCP - Neuroimaging, Bristol-Myers Squibb, Wallingford, CT, United States, ³Neurosurgery, Pennsylvania State University - College of Medicine, Hershey, PA, United States

Our previous research illuminated that transverse relaxation and contrast related to A plaques seen in T₂* weighted images are associated with plaque morphology and iron content. The fibrillar organization and macromolecule architecture of A plaques presents an ideal setting for the usage of magnetization transfer (MT) imaging to view tightly bond protons on the surface of component fibrils in the A plaques. The data demonstrate the detailed MT associated with individual plaques and the different MT ratios of A ROI’s compared to surrounding tissue. To our knowledge, this data represents the first MT imaging of individual A plaques.

Gwenaelle Douaud¹, Ricarda Menke¹, Achim Gass², Andreas Monsch³, Marc Sollberger^2,3, Anil Rao⁴, Brandon Whitcher⁴, Paul Matthews⁴, and Stephen Smith^1
1FMRIB Centre, University of Oxford, Oxford, Oxfordshire, United Kingdom, ²Departments of Neurology and Neuroradiology, University Hospital Basel, Switzerland,³Memory Clinic, Department of Geriatrics, University Hospital Basel, Switzerland, ⁴GlaxoSmithKline,Clinical Imaging Centre, Hammersmith Hospital London

We investigated for the first time grey and white matter differences at baseline between “stable” amnestic MCI patients and those who later converted to AD. Importantly, we focused on late conversion, as patients converted at least two years after their scan. Using FSL-VBM and TBSS, we found significantly reduced GM mainly in the striatum and the left hippocampus and increased FA where the SLF crosses the CST in the MCI converters. Remarkably, these white matter microstructural alterations detected in crossing-fibre tracts using diffusion imaging proved more sensitive to predict conversion to AD.

Kenichi Oishi¹, Michelle M Mielke², Andreia V Faria¹, Michael I Miller³, Perer C.M. van Zijl^3,4, Marilyn Albert^5,6, Constantine G Lyketsos^2,6, and Susumu Mori^1,4
1Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Psychiatry and Behavioral Sciences, Johns Hopkins University, ³Johns Hopkins University, ⁴Kennedy Krieger Institute, ⁵Neurology, Johns Hopkins University, ⁶The Johns Hopkins Alzheimer’s Disease Research Center

We have developed an image analysis tool in which information extracted from multiple MRI modalities, using disease-specific spatial filters, is combined to generate a disease score. This tool was tested as an automated method to predict the conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s disease (AD). We created disease-specific filters for each modality and optimized the combination to separate AD from control participants, using a training dataset, and applied the tool to calculate disease scores of 22 aMCI patients. The disease score predicted the conversion better than a single-modality approach, indicating the potential value for clinical application.

Giovanni Giulietti¹, Marco Bozzali¹, Viviana Figura¹, Roberta Perri², Camillo Marra³, Franco Giubilei⁴, and Mara Cercignani^1
1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, ²Department of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy, ³Institute of Neurology, Cattolica University, Rome, Italy, ⁴Department of Neurology, Sapienza University, Rome, Italy

Quantitative magnetisation transfer imaging (qMTI) is an extension of MTI which allows the binary spin bath model parameters to be estimated. In this study we have extended the assessment of qMT parameters in patients with Alzheimer’s disease (AD) to the whole brain and determined the joint contribution of gray matter regional atrophy and qMT parameters for the classification of AD using a logistic regression analysis. Our results indicate that a decrese of RM_0B (forward exchange rate) in the hipoccampal/parahippocampal areas, in the posterior cingulate, and in the posterior parietal cortex is significantly predictive of AD diagnosis.

Matthew C Murphy¹, John Huston, III¹, Clifford R Jack, Jr.¹, Kevin J Glaser¹, Armando Manduca¹, Joel P Felmlee¹, and Richard L Ehman^1
1Department of Radiology, Mayo Clinic, Rochester, MN, United States

MRE is a technique for noninvasively measuring tissue stiffness. The purpose of this work was to assess reproducibility of a 3D MRE exam of the brain in 10 healthy volunteers, and to use the 3D brain MRE exam to study the effects of Alzheimer’s disease (AD) in 7 subjects with probable AD, 14 age- and gender-matched PIB- controls and 7 age- and gender-matched PIB+ controls. MRE detected a significant decrease in brain stiffness in subjects with AD compared to both control groups. This decrease in stiffness likely reflects a loss of normal cytoarchitecture of the brain parenchyma due to AD.